The ASAP II database: analysis and comparative genomics of alternative splicing in 15 animal species

We have greatly expanded the Alternative Splicing Annotation Project (ASAP) database: (i) its human alternative splicing data are expanded ∼3-fold over the previous ASAP database, to nearly 90 000 distinct alternative splicing events; (ii) it now provides genome-wide alternative splicing analyses for 15 vertebrate, insect and other animal species; (iii) it provides comprehensive comparative genomics information for comparing alternative splicing and splice site conservation across 17 aligned genomes, based on UCSC multigenome alignments; (iv) it provides an ∼2- to 3-fold expansion in detection of tissue-specific alternative splicing events, and of cancer versus normal specific alternative splicing events. We have also constructed a novel database linking orthologous exons and orthologous introns between genomes, based on multigenome alignment of 17 animal species. It can be a valuable resource for studies of gene structure evolution. ASAP II provides a new web interface enabling more detailed exploration of the data, and integrating comparative genomics information with alternative splicing data. We provide a set of tools for advanced data-mining of ASAP II with Pygr (the Python Graph Database Framework for Bioinformatics) including powerful features such as graph query, multigenome alignment query, etc. ASAP II is available at

Organizational and managerial aspects of professional sports in the Republic of Belarus

The study is devoted to the economic development of the sports industry in the Republic of Belarus. Special attention is paid to the approaches to the development of professional sports in Europe and the USA. The advantages and disadvantages of the existing systems of professional sports are noted. The results of the study show great differences between the system of professional sports in the Republic of Belarus and the existing ones in the USA and Europe

Organizational and managerial aspects of professional sports in the Republic of Belarus

The study is devoted to the economic development of the sports industry in the Republic of Belarus. Special attention is paid to the approaches to the development of professional sports in Europe and the USA. The advantages and disadvantages of the existing systems of professional sports are noted. The results of the study show great differences between the system of professional sports in the Republic of Belarus and the existing ones in the USA and Europe

OHMI: The Ontology of Host-Microbiome Interactions

Host-microbiome interactions (HMIs) are critical for the modulation of biological processes and are associated with several diseases, and extensive HMI studies have generated large amounts of data. We propose that the logical representation of the knowledge derived from these data and the standardized representation of experimental variables and processes can foster integration of data and reproducibility of experiments and thereby further HMI knowledge discovery. A community-based Ontology of Host-Microbiome Interactions (OHMI) was developed following the OBO Foundry principles. OHMI leverages established ontologies to create logically structured representations of microbiomes, microbial taxonomy, host species, host anatomical entities, and HMIs under different conditions and associated study protocols and types of data analysis and experimental results

Causal graph-based analysis of genome-wide association data in rheumatoid arthritis

<p>Abstract</p> <p>Background</p> <p>GWAS owe their popularity to the expectation that they will make a major impact on diagnosis, prognosis and management of disease by uncovering genetics underlying clinical phenotypes. The dominant paradigm in GWAS data analysis so far consists of extensive reliance on methods that emphasize contribution of individual SNPs to statistical association with phenotypes. Multivariate methods, however, can extract more information by considering associations of multiple SNPs simultaneously. Recent advances in other genomics domains pinpoint multivariate causal graph-based inference as a promising principled analysis framework for high-throughput data. Designed to discover biomarkers in the local causal pathway of the phenotype, these methods lead to accurate and highly parsimonious multivariate predictive models. In this paper, we investigate the applicability of causal graph-based method TIE* to analysis of GWAS data. To test the utility of TIE*, we focus on anti-CCP positive rheumatoid arthritis (RA) GWAS datasets, where there is a general consensus in the community about the major genetic determinants of the disease.</p> <p>Results</p> <p>Application of TIE* to the North American Rheumatoid Arthritis Cohort (NARAC) GWAS data results in six SNPs, mostly from the MHC locus. Using these SNPs we develop two predictive models that can classify cases and disease-free controls with an accuracy of 0.81 area under the ROC curve, as verified in independent testing data from the same cohort. The predictive performance of these models generalizes reasonably well to Swedish subjects from the closely related but not identical Epidemiological Investigation of Rheumatoid Arthritis (EIRA) cohort with 0.71-0.78 area under the ROC curve. Moreover, the SNPs identified by the TIE* method render many other previously known SNP associations conditionally independent of the phenotype.</p> <p>Conclusions</p> <p>Our experiments demonstrate that application of TIE* captures maximum amount of genetic information about RA in the data and recapitulates the major consensus findings about the genetic factors of this disease. In addition, TIE* yields reproducible markers and signatures of RA. This suggests that principled multivariate causal and predictive framework for GWAS analysis empowers the community with a new tool for high-quality and more efficient discovery.</p> <p>Reviewers</p> <p>This article was reviewed by Prof. Anthony Almudevar, Dr. Eugene V. Koonin, and Prof. Marianthi Markatou.</p

Simrank: Rapid and sensitive general-purpose k-mer search tool

Terabyte-scale collections of string-encoded data are expected from consortia efforts such as the Human Microbiome Project (http://nihroadmap.nih.gov/hmp). Intra- and inter-project data similarity searches are enabled by rapid k-mer matching strategies. Software applications for sequence database partitioning, guide tree estimation, molecular classification and alignment acceleration have benefited from embedded k-mer searches as sub-routines. However, a rapid, general-purpose, open-source, flexible, stand-alone k-mer tool has not been available. Here we present a stand-alone utility, Simrank, which allows users to rapidly identify database strings the most similar to query strings. Performance testing of Simrank and related tools against DNA, RNA, protein and human-languages found Simrank 10X to 928X faster depending on the dataset. Simrank provides molecular ecologists with a high-throughput, open source choice for comparing large sequence sets to find similarity

Strategic Applications of Gene Expression: From Drug Discovery/Development to Bedside

ABSTRACT. Gene expression is useful for identifying the molecular signature of a disease and for correlating a pharmacodynamic marker with the dose-dependent cellular responses to exposure of a drug. Gene expression offers utility to guide drug discovery by illustrating engagement of the desired cellular pathways/networks, as well as avoidance of acting on the toxicological pathways. Successful employment of gene-expression signatures in the later stages of drug development depends on their linkage to clinically meaningful phenotypic characteristics and requires a biologically meaningful mechanism combined with a stringent statistical rigor. Much of the success in clinical drug development is hinged on predefining the signature genes for their fitness for purposes of application. Specific examples are highlighted to illustrate the breadth and depth of the potential utility of gene-expression signatures in drug discovery and clinical development to targeted therapeutics at the bedside